Advanced machine learning methods for wind erosion monitoring in southern Iran

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Rezaei Mahrooz; sameni abdolmajid; FALLAH SHAMSI SEYED RASHID

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Journal: Environmental Erosion Research Year:2018 | Volume:8 | Issue:1 (29) Page(s): 39-58

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Information Journal Paper

Title

Advanced machine learning methods for wind erosion monitoring in southern Iran

Author(s)

Rezaei Mahrooz | sameni abdolmajid | FALLAH SHAMSI SEYED RASHID | Issue Writer Certificate

Keywords

Wind ErosionQ2

Land use/coverQ1

LandsatQ1

Support Vector MachineQ1

Abstract

1-Introduction: Wind Erosion is one the most important factors of land degradation in the arid and semi-arid areas and it is one the most serious environmental problems in the world. In Fars province, 17 cities are prone to Wind Erosion and are considered as critical zones of Wind Erosion. One of the most important factors in soil Wind Erosion is Land use/cover change. Therefore, accurate mapping of Land use/cover and Wind Erosion evidences in arid and semiarid regions is the utmost importance. Moreover, for discrimination of land covers resulting from Wind Erosion such as sand sheets and Nebka, we need accurate remote sensing methods. In this study, capability of the advanced machine learning techniques on Landsat 7 and 8 imageries in mapping Land use/cover related to Wind Erosion is evaluated.....

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APA: Copy

Rezaei, Mahrooz, sameni, abdolmajid, & FALLAH SHAMSI, SEYED RASHID. (2018). Advanced machine learning methods for wind erosion monitoring in southern Iran. ENVIRONMENTAL EROSION RESEARCHES, 8(1 (29) ), 39-58. SID. https://sid.ir/paper/399605/en

Vancouver: Copy

Rezaei Mahrooz, sameni abdolmajid, FALLAH SHAMSI SEYED RASHID. Advanced machine learning methods for wind erosion monitoring in southern Iran. ENVIRONMENTAL EROSION RESEARCHES[Internet]. 2018;8(1 (29) ):39-58. Available from: https://sid.ir/paper/399605/en

IEEE: Copy

Mahrooz Rezaei, abdolmajid sameni, and SEYED RASHID FALLAH SHAMSI, “Advanced machine learning methods for wind erosion monitoring in southern Iran,” ENVIRONMENTAL EROSION RESEARCHES, vol. 8, no. 1 (29) , pp. 39–58, 2018, [Online]. Available: https://sid.ir/paper/399605/en

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